Category Archives: Mahout

What a short, strange trip it’s been. Just a year ago, I founded Myrrix in London’s Silicon Roundabout to commercialize large-scale machine learning based on Apache Hadoop and Apache Mahout. It’s been a busy scramble, building software and proudly watching early customers get real, big data-sized machine learning into production.

And now another beginning: Myrrix has a new home in Cloudera. I’m excited to join as Director of Data Science in London,

Editor’s note (12/19/2013): Cloudera ML has been merged into the Oryx project. The information below is still valid though.

Last month, Apache Crunch became the fifth project (along with Sqoop, Flume, Bigtop, and MRUnit) to go from Cloudera’s github repository through the Apache Incubator and on to graduate as a top-level project within the Apache Software Foundation. As the founder of the project and a newly minted Apache VP,

Cloudera recently announced the general availability of CDH4.1, an update to our open-source, enterprise-ready distribution of Apache Hadoop and related projects. Among various components, Apache Mahout is a relatively recent addition to CDH (first added to CDH3u2 in 2011), but is already attracting increasing interest out in the field.

Mahout started as a sub-project of Apache Lucene to provide machine-learning libraries in the area of clustering and classification. It later evolved into a top-level Apache project with much broader coverage of machine-learning techniques (clustering,

This guest post is provided by Dan McClary, Principal Product Manager for Big Data and Hadoop at Oracle.

One of the constants in discussions around Big Data is the desire for richer analytics and models. However, for those who don’t have a deep background in statistics or machine learning, it can be difficult to know not only just what techniques to apply, but on what data to apply them.